Systems and methods for increasing resolution of images captured by a camera sensor

ABSTRACT

Systems, methods, and non-transitory computer readable media can obtain information relating to a bias associated with a camera sensor. A plurality of images of a scene captured by the camera sensor can be obtained, where the plurality of images are captured at a resolution supported by the camera sensor. A plurality of weights for each image of the plurality of images can be determined based at least in part on the bias. A combined image of the scene can be generated based on the plurality of images and the determined weights, the combined image having a resolution higher than the resolution supported by the camera sensor.

FIELD OF THE INVENTION

The present technology relates to image capture and generation. Moreparticularly, the present technology relates to techniques forincreasing resolution of captured images.

BACKGROUND

Today, people often utilize computing devices (or systems) for a widevariety of purposes. Users can use their computing devices, for example,to interact with one another, create content, share content, and viewcontent. In some cases, a user can utilize his or her computing deviceto access a social networking system (or service). The user can provide,post, share, and access various content items, such as status updates,images, videos, articles, and links, via the social networking system.

A social networking system may provide resources through which users maypublish content items. In one example, a content item can be presentedon a profile page of a user. As another example, a content item can bepresented through a feed for a user to access. Before publication of acontent item, the social networking system can apply processingtechniques to the content item for optimal presentation.

SUMMARY

Various embodiments of the present disclosure can include systems,methods, and non-transitory computer readable media configured to obtaininformation relating to a bias associated with a camera sensor. Aplurality of images of a scene captured by the camera sensor can beobtained, where the plurality of images are captured at a resolutionsupported by the camera sensor. A plurality of weights for each image ofthe plurality of images can be determined based at least in part on thebias. A combined image of the scene can be generated based on theplurality of images and the determined weights, the combined imagehaving a resolution higher than the resolution supported by the camerasensor.

In some embodiments, the bias provides subpixel image data for a portionof an image captured by the camera sensor.

In certain embodiments, the bias includes one or more of: a directionalbias, a random bias, or an average bias.

In an embodiment, the bias is the directional bias and the plurality ofimages are not aligned, and a location of the bias with respect to thescene moves across the plurality of images.

In some embodiments, the bias is the random bias and the plurality ofimages are aligned, and each image of the plurality of images includes arandom set of pixels captured by the camera sensor.

In certain embodiments, the plurality of weights for each image includesa weight for each pixel of the image.

In an embodiment, the plurality of weights for each image is determinedbased on a machine learning model.

In some embodiments, for a particular section of the combined image, aweight assigned to a portion of an image of the plurality of images thataligns with the particular section is higher than a weight assigned to aportion of an image of the plurality of images that does not align withthe particular section.

In certain embodiments, a weight assigned to a portion of an image ofthe plurality of images that includes the bias is higher than a weightassigned to a portion of the image that does not include the bias.

In an embodiment, the generating the combined image of the sceneincludes performing a join on the plurality of images and thecorresponding plurality of weights.

It should be appreciated that many other features, applications,embodiments, and/or variations of the disclosed technology will beapparent from the accompanying drawings and from the following detaileddescription. Additional and/or alternative implementations of thestructures, systems, non-transitory computer readable media, and methodsdescribed herein can be employed without departing from the principlesof the disclosed technology.

Various embodiments of the present disclosure can include systems,methods, and non-transitory computer readable media configured toprovide a vibrating source configured to cause movement of a camerasensor to generate a bias for the camera sensor. A high frequencyaccelerometer configured to measure movement of the camera sensor can beprovided. A plurality of images of a scene can be captured using thecamera sensor based on the generated bias at a resolution supported bythe camera sensor, wherein the high frequency accelerometer measuresmovement of the camera sensor during the capture of the plurality ofimages.

In some embodiments, the camera sensor includes one or more of: a chargecoupled device (CCD) sensor or a complementary metal oxide semiconductor(CMOS) sensor.

In certain embodiments, the vibrating source is a spindle with a weightthat is off center.

In an embodiment, the vibrating source and the high frequencyaccelerometer are coupled to the camera sensor.

In some embodiments, the generated bias is a directional bias.

In certain embodiments, the bias provides subpixel image data for aportion of an image captured by the camera sensor.

In an embodiment, a combined image having a higher resolution than theresolution supported by the camera sensor is generated based on theplurality of images and weights associated with the plurality of images.

In some embodiments, the weights associated with the plurality of imagesinclude a plurality of weights associated with each image of theplurality of images.

In certain embodiments, for a particular section of the combined image,a weight assigned to a portion of an image of the plurality of imagesthat aligns with the particular section is higher than a weight assignedto a portion of an image of the plurality of images that does not alignwith the particular section.

In an embodiment, a weight assigned to a portion of an image of theplurality of images that includes the bias is higher than a weightassigned to a portion of the image that does not include the bias.

It should be appreciated that many other features, applications,embodiments, and/or variations of the disclosed technology will beapparent from the accompanying drawings and from the following detaileddescription. Additional and/or alternative implementations of thestructures, systems, non-transitory computer readable media, and methodsdescribed herein can be employed without departing from the principlesof the disclosed technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system including an example highresolution image module configured to increase resolution of capturedimages, according to an embodiment of the present disclosure.

FIG. 2A illustrates an example captured image combination moduleconfigured to generate high resolution images from captured images,according to an embodiment of the present disclosure.

FIG. 2B illustrates an example bias generation module configured togenerate a bias associated with a camera sensor, according to anembodiment of the present disclosure.

FIG. 3A illustrates a functional block diagram for increasing resolutionof captured images, according to an embodiment of the presentdisclosure.

FIG. 3B illustrates an example computing device for generating a biasassociated with a camera sensor, according to an embodiment of thepresent disclosure.

FIG. 4 illustrates an example first method for increasing resolution ofcaptured images, according to an embodiment of the present disclosure.

FIG. 5 illustrates an example second method for increasing resolution ofcaptured images, according to an embodiment of the present disclosure.

FIG. 6 illustrates a network diagram of an example system that can beutilized in various scenarios, according to an embodiment of the presentdisclosure.

FIG. 7 illustrates an example of a computer system that can be utilizedin various scenarios, according to an embodiment of the presentdisclosure.

The figures depict various embodiments of the disclosed technology forpurposes of illustration only, wherein the figures use like referencenumerals to identify like elements. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated in the figures can be employedwithout departing from the principles of the disclosed technologydescribed herein.

DETAILED DESCRIPTION

Increasing Effective Resolution of Images Captured by Camera Sensors

People use computing devices (or systems) for a wide variety ofpurposes. Computing devices can provide different kinds offunctionality. Users can utilize their computing devices to produceinformation, access information, and share information. In some cases,users can utilize computing devices to interact or engage with aconventional social networking system (e.g., a social networkingservice, a social network, etc.). A social networking system may provideresources through which users may publish content items. In one example,a content item can be presented on a profile page of a user. As anotherexample, a content item can be presented through a feed for a user toaccess.

Users can upload various types of media content items, such as imagesand videos, to a social networking system. For example, users cancapture images on computing devices using a camera. Under conventionalapproaches specifically arising in the realm of computer technology,images are captured on a computing device (e.g., a client computingdevice) at a resolution supported by a camera sensor on the computingdevice. For example, an image captured on a computing device can beprovided at a highest resolution supported by a camera sensor of thecomputing device, or a resolution that is lower than the highestresolution. Accordingly, the resolution of an image captured on acomputing device can be limited by physical properties of a camerasensor on the computing device.

An improved approach rooted in computer technology can overcome theforegoing and other disadvantages associated with conventionalapproaches specifically arising in the realm of computer technology.Based on computer technology, the disclosed technology can increaseresolution of images captured on a computing device by a camera sensorbased on multiple captured images and a bias associated with the camerasensor. A camera sensor can have an associated bias. For example, thebias can exist due to or result from physical properties of the camerasensor. The disclosed technology can obtain subpixel image data based onthe bias in multiple captured images of a scene. Weights can bedetermined for each of the multiple captured images of the scene, andthe weights can be used to combine the multiple captured images togenerate an image that has a higher resolution than a resolutionsupported by the camera sensor. For example, the weights can bedetermined based on the bias in the multiple captured images and/orportions of a high resolution image to which the multiple capturedimages map. In some embodiments, the weights can be determined based onmachine learning techniques. In this manner, the disclosed technologycan increase an effective resolution of a camera sensor. Additionaldetails relating to the disclosed technology are provided below.

FIG. 1 illustrates an example system 100 including an example highresolution image module 102 configured to increase resolution ofcaptured images, according to an embodiment of the present disclosure.The high resolution image module 102 can include a bias informationmodule 104 and a captured image combination module 106. The examplesystem 100 can also include a computing device 110, such as a clientcomputing device. The computing device 110 can include a bias generationmodule 112. In some embodiments, the high resolution image module 102can communicate with the computing device 110. In some instances, theexample system 100 can include at least one data store 120. Thecomponents (e.g., modules, elements, steps, blocks, etc.) shown in thisfigure and all figures herein are exemplary only, and otherimplementations may include additional, fewer, integrated, or differentcomponents. Some components may not be shown so as not to obscurerelevant details. In various embodiments, one or more of thefunctionalities described in connection with the high resolution imagemodule 102 can be implemented in any suitable combinations. While thedisclosed technology is described in connection with captured imagesassociated with a social networking system for illustrative purposes,the disclosed technology can apply to any other type of system and/orcontent.

The bias information module 104 can determine or obtain informationrelating to a bias associated with a camera sensor. A bias associatedwith a camera sensor can exist due to or result from physical propertiesof the camera sensor. For example, a camera sensor can have a bias incapturing photons or recording pixels. There can be different types ofbiases. As an example, the bias can be directional. For instance, thecamera sensor can record a pixel in a particular location or region(e.g., upper left, upper right, lower left, lower right, etc.). Asanother example, the bias can be random, and the camera sensor canrecord a random subset of pixels. As an additional example, the bias canbe an average of pixels. The bias associated with a camera sensor canvary depending on the camera sensor. The bias determination module 104can determine or obtain information on what type of bias is associatedwith a particular camera sensor. The type of bias associated with thecamera sensor can be used in combining multiple images captured by thecamera sensor, as described below. In some embodiments, a bias canrepresent information about a subsampling (e.g., ordered, random, etc.)of a low resolution image. For example, a low resolution image can be animage captured by a camera sensor. Knowledge of how the bias correspondto the subsampling can allow generation of a higher resolution imagebased on one or more low resolution images. For example, weightsassociated with one or more low resolution images may indicate how thebias corresponds to the subsampling.

In some embodiments, a bias can be generated for a camera sensor basedon hardware components that are external to the camera sensor. Forexample, the generated bias can be different from a bias associated witha camera sensor. In these embodiments, the bias determination module 104can determine or obtain information relating to the generated bias. Incertain embodiments, a bias can be generated by the bias generationmodule 112, as described below. All examples herein are provided forillustrative purposes, and there can be many variations and otherpossibilities.

The captured image combination module 106 can generate high resolutionimages from captured images. Multiple images of a scene captured by acamera sensor at a resolution supported by the camera sensor can beobtained. The captured image combination module 106 can determineweights associated with the multiple captured images to be used incombining the multiple captured images to generate a high resolutionimage. A high resolution image can be generated based on the multiplecaptured images and the corresponding weights for the multiple capturedimages. Functionality of the captured image combination module 106 isdescribed in more detail herein.

The bias generation module 112 can generate a bias associated with acamera sensor. In some embodiments, a bias can be generated for a camerasensor based on hardware components. For example, the generated bias canbe a bias that is separate from a bias associated with a camera sensor.The bias generation module 112 can reside on the computing device 110.The computing device 110 can be a client computing device, such as auser device 610 in FIG. 6. Functionality of the bias generation module112 is described in more detail herein.

In some embodiments, the high resolution image module 102 and/or thebias generation module 112 can be implemented, in part or in whole, assoftware, hardware, or any combination thereof. In general, a module asdiscussed herein can be associated with software, hardware, or anycombination thereof. In some implementations, one or more functions,tasks, and/or operations of modules can be carried out or performed bysoftware routines, software processes, hardware, and/or any combinationthereof. In some cases, the high resolution image module 102 and/or thebias generation module 112 can be, in part or in whole, implemented assoftware running on one or more computing devices or systems, such as ona server system or a client computing device. In some instances, thehigh resolution image module 102 and/or the bias generation module 112can be, in part or in whole, implemented within or configured to operatein conjunction or be integrated with a social networking system (orservice), such as a social networking system 630 of FIG. 6. Likewise, insome instances, the high resolution image module 102 and/or the biasgeneration module 112 can be, in part or in whole, implemented within orconfigured to operate in conjunction or be integrated with a clientcomputing device, such as the user device 610 of FIG. 6. For example,the high resolution image module 102 and/or the bias generation module112 can be implemented as or within a dedicated application (e.g., app),a program, or an applet running on a user computing device or clientcomputing system. It should be understood that many variations arepossible.

The data store 120 can be configured to store and maintain various typesof data, such as the data relating to support of and operation of thehigh resolution image module 102 and/or the bias generation module 112.The data maintained by the data store 120 can include, for example,information relating to camera sensors, biases, captured images, weightsassociated with captured images, high resolution images generated fromcaptured images, etc. The data store 120 also can maintain otherinformation associated with a social networking system. The informationassociated with the social networking system can include data aboutusers, social connections, social interactions, locations, geo-fencedareas, maps, places, events, groups, posts, communications, content,account settings, privacy settings, and a social graph. The social graphcan reflect all entities of the social networking system and theirinteractions. As shown in the example system 100, the high resolutionimage module 102 can be configured to communicate and/or operate withthe data store 120. In some embodiments, the bias generation module 112can also be configured to communicate and/or operate with the data store120. In some embodiments, the data store 120 can be a data store withina client computing device. In some embodiments, the data store 120 canbe a data store of a server system in communication with the clientcomputing device.

FIG. 2A illustrates an example captured image combination module 202configured to generate high resolution images from captured images,according to an embodiment of the present disclosure. In someembodiments, the captured image combination module 106 of FIG. 1 can beimplemented with the example captured image combination module 202. Asshown in the example of FIG. 2A, the example captured image combinationmodule 202 can include a weight determination module 204 and a highresolution image generation module 206.

The captured image combination module 202 can obtain multiple images ofa scene captured by a camera sensor at a resolution supported by thecamera sensor. For example, a resolution supported by the camera sensorcan be a pixel resolution of the camera sensor. A resolution supportedby the camera sensor can be referred to as a “camera sensor resolution”or “low resolution,” as discussed herein. Images captured by the camerasensor at a resolution supported by the camera sensor can be referred toas “camera sensor resolution images” or “low resolution images.” Themultiple captured images of the scene can be combined to generate acombined image of the scene that has a higher resolution than themultiple captured images. In some embodiments, the higher resolutioncombined image of the scene generated from the multiple captured imagesof the scene can be referred to as the “high resolution image.”

Each captured image of multiple captured images of a scene can depict aspecific portion of the scene. The multiple captured images can becombined to generate a high resolution image. In some instances, thelocation or position of a camera sensor may move with respect to a sceneas multiple captured images of the scene are generated such that not allof the multiple captured images capture the same portion of the scene.In these instances, the multiple captured images are not aligned. Toalign the multiple captured images so that their combination coherentlydepicts the scene, each captured image can be translated based on theportion of the scene depicted in the captured image to map to acorresponding portion of the scene depicted in the high resolutionimage. In other instances, the location or position of the camera sensormay stay constant with respect to a scene as multiple captured images ofthe scene are generated. In these instances, the multiple capturedimages are aligned, and each captured image can map to the highresolution image without a need for translation.

In some embodiments, the high resolution image can be represented as agrid of pixels, which can be referred to as a “high resolution imagepixel grid.” The high resolution image pixel grid can have a higherresolution than a resolution supported by the camera sensor. Forexample, the high resolution image pixel grid can be a virtual grid thathas a higher number of pixels than a number of pixels of the camerasensor or a number of pixels in the multiple captured images.Accordingly, a pixel in a captured image can map to more than one pixelin the high resolution image. Weights can be determined for eachcaptured image, and the multiple captured images can be combined basedon the respective weights to generate the high resolution image.

A bias associated with a camera sensor can be used to obtain subpixelimage data for a scene. Subpixel image data can refer to image data at ahigher resolution than a resolution supported by the camera sensor(e.g., a pixel resolution of the camera sensor). The bias can providesubpixel image data in a captured image. In an example, if the bias isdirectional (e.g., towards upper left), the bias can provide subpixelimage data in a particular region of a captured image (e.g., towardsupper left). If the camera sensor is moved slightly each time an imageof the scene is captured, a portion of the scene depicted can changeacross different captured images, and the location of the bias withrespect to the scene can also move in the different captured images. Forexample, the bias may move consistently with respect to the scene acrossthe different captured images. Accordingly, multiple captured images canprovide subpixel image data for different parts of the scene over time.In this example, the multiple captured images of the scene may not bealigned perfectly since the camera sensor moves over time. In anotherexample, if the bias is random, the camera sensor can record a randomset of pixels for a captured image. Accordingly, the location of thebias can also move over time in different captured images. In thisexample, the bias can provide subpixel image data for different parts ofa scene even though the camera sensor is not moved since random pixelsare recorded. Therefore, in this example, the multiple captured imagesof the scene could be aligned. The bias in each captured image can beused to determine weights associated with each captured image that canbe used in combining multiple captured images.

The weight determination module 204 can determine weights associatedwith multiple captured images to be used in combining the multiplecaptured images to generate a high resolution image. Weights associatedwith multiple captured images can be determined based on the biasassociated with the camera sensor and/or to which portions of a highresolution image the multiple captured images map. The weightdetermination module 204 can determine weights for each of multiplecaptured images. For example, the weight determination module 204 candetermine a set of weights associated with each captured image. Forinstance, there can be a weight for each pixel of a captured image. Aweight associated with a pixel can indicate importance of a pixel in afirst captured image compared to a pixel in a second captured image ingenerating a high resolution image. For instance, the weight associatedwith the pixel in the first captured image and the weight associatedwith the pixel in the second captured image can indicate respectiveimportance of the pixels in determining the value of one or morecorresponding pixels in the high resolution image. In some cases, aweight associated with a pixel can also indicate importance of a firstpixel in a captured image compared to a second pixel in the samecaptured image in generating a high resolution image. For instance,weights can vary for different pixels within the same captured image. Insome embodiments, there can be multiple sets of weights and/or levels ofweights for each captured image.

The weights for each captured image can be used in combining multiplecaptured images to generate a combined high resolution image. Forexample, if the bias is a directional bias (e.g., toward upper left) andmultiple captured images are not aligned, each captured image can depicta different portion of a scene. As described above, the multiplecaptured images can be translated and mapped to a corresponding portionof the scene in a high resolution image. Weights for each captured imagecan be determined based on how the captured image maps to the highresolution image. For instance, a captured image that aligns with aparticular section of the high resolution image can be weighted morehighly than a captured image that does not align with that particularsection. A captured image may align with the particular section of thehigh resolution image if a portion of the scene depicted in the capturedimage perfectly maps to or aligns with a portion of the scene depictedin the particular section of the high resolution image. In addition, aportion of a captured image where the bias is located can be weightedmore highly than the rest of the captured image. For instance, a portionof a captured image that provides subpixel image data for a particularsection of the high resolution image can be weighted more highly than aportion of the captured image or a portion of another captured imagethat does not provide subpixel image data for the particular section ofthe high resolution image. For a directional bias, a set of simple ornonrandom weights may be used in some embodiments. As another example,if the bias is a random bias, multiple captured images may be aligned,and each captured image can be mapped to a high resolution image withoutbeing translated. A portion of a captured image where the bias islocated can be weighted more highly than the rest of the captured image.For a random bias, more random weights can be used. In some embodiments,the weights can be determined and tuned manually.

In some embodiments, the weight determination module 204 can determineweights associated with a captured image based on machine learningtechniques. For example, a machine learning model can be trained basedon training data including multiple captured images of scenes,corresponding high resolution images of scenes, and/or bias. The trainedmachine learning model can predict weights for a particular set ofmultiple captured images. In some embodiments, the machine learningmodel can be trained to determine a bias associated with a camera sensorthat captured multiple captured images and predict weights for themultiple captured images based on the bias. In some embodiments, themachine learning model can be a neural network. In some embodiments, amachine learning model can be trained for a particular camera sensor.Accordingly, there can be multiple machine learning models, each trainedfor a particular camera sensor. One or more machine learning modelsdiscussed in connection with the high resolution image module 102 andits components can be implemented separately or in combination, forexample, as a single machine learning model, as multiple machinelearning models, as one or more staged machine learning models, as oneor more combined machine learning models, etc.

The high resolution image generation module 206 can generate a highresolution image based on multiple captured images and correspondingweights for the multiple captured images. In an example, each capturedimage can have a set of weights associated with the image, and apairwise join can be performed on each captured image and the set ofweights. The multiple captured images can be combined to generate acombined high resolution image. A type of join and/or other operationsperformed to generate a high resolution image can vary depending on thecamera sensor and/or the bias. For example, a join and/or otheroperations performed can be adapted for a particular camera sensor.

In this manner, the disclosed technology can obtain subpixel image databased on multiple captured images of a scene and a bias associated witha camera sensor used to capture the images of the scene, and generate acombined high resolution image. The disclosed technology can thusgenerate images that have a resolution that exceeds physical limits ofthe camera sensor and can improve an effective resolution of the camerasensor. The disclosed technology can have various applications. As anexample, the disclosed technology can be used in connection withsatellites. When a satellite having a camera sensor is above a certainheight from a surface, it can be difficult to capture high qualityimages of the surface using a resolution supported by the camera sensor,and the disclosed technology can be used to improve the effectiveresolution of the camera sensor. For example, a combined high resolutionimage can be generated based on multiple captured images of the surface.Generating the combined high resolution image can be performed based oncomputing or processing resources, such as central processing units(CPUs), without having to add camera sensors that support higherresolutions. All examples herein are provided for illustrative purposes,and there can be many variations and other possibilities.

FIG. 2B illustrates an example bias generation module 252 configured togenerate a bias associated with a camera sensor, according to anembodiment of the present disclosure. In some embodiments, the biasgeneration module 112 of FIG. 1 can be implemented with the example biasgeneration module 252. As shown in the example of FIG. 2B, the examplebias generation module 252 can include a camera sensor 254, a vibratingsource 256, and a high frequency accelerometer 258. The bias generationmodule 252 can reside on a computing device of a user, such as a cameraor a mobile phone. The vibrating source 256 and the high frequencyaccelerometer 258 can be connected or paired with the camera sensor 254.In some cases, a bias associated with a camera sensor may not be known,and the bias generation module 252 can introduce a bias for the camerasensor in order to generate subpixel image data.

The camera sensor 254 can capture images of a scene. For example,photons can pass through a lens, and the camera sensor 254 can capturethe photons to generate images at a resolution supported by the camerasensor 254. In some embodiments, the camera sensor 254 can be a chargecoupled device (CCD) sensor. In other embodiments, the camera sensor 254can be a complementary metal oxide semiconductor (CMOS) sensor. Manyvariations are possible.

The vibrating source 256 can generate movement of the camera sensor 254in order to generate the bias. As an example, the vibrating source 256can introduce a directional bias for the camera sensor 254. Thevibrating source 256 can introduce a slight movement in the camerasensor 254 such that each image of a scene captured by the camera sensor254 depicts a different portion of the scene. The vibrating source 256can introduce a subpixel movement such that the camera sensor 254 ismoved by a subpixel distance. For instance, subpixel image data can begenerated if a distance the camera sensor 254 is moved is less than thesize of a pixel of the camera sensor 254. In some embodiments, thevibrating source 256 can be a weight on a spindle that is off center.Many variations are possible.

The high frequency accelerometer 258 can measure movement of the camerasensor 254 due to movement generated by the vibrating source 256. Forinstance, the high frequency accelerometer 258 can measure a distance orextent and/or a direction of a movement. As an example, the highfrequency accelerometer 258 can measure a location or a movement of thecamera sensor 254 on an order of a thousand times per second. Manyvariations are possible.

The bias generation module 112 can determine the generated biasintroduced by the vibrating source 256 based on measurements by the highfrequency accelerometer 258. Images capturing a scene with the generatedbias can include a directional bias, and the directional bias can beused in generating a high resolution image from multiple capturedimages, as described above in connection with the high resolution imagemodule 102. In this manner, the bias generation module 112 can be usedin connection with the high resolution image module 102. All examplesherein are provided for illustrative purposes, and there can be manyvariations and other possibilities.

FIG. 3A illustrates a functional block diagram 300 for increasingresolution of captured images, according to an embodiment of the presentdisclosure. Captured images 302 a, 302 b, 302 c can be multiple imagesof a scene captured by a camera sensor. The captured images 302 a, 302b, 302 c can each include a bias associated with the camera sensor. Atblock 304, weights can be determined for the captured images 302 a, 302b, 302 c. For example, weights can be determined as described above inconnection with the high resolution image module 102. Weights forcaptured image 306 a can be weights associated with the captured image302 a; weights for captured image 306 b can be weights associated withthe captured image 302 b; and weights for captured image 306 c can beweights associated with the captured image 302 c. Weights for a capturedimage can include a weight for each pixel of the captured image. Atblock 306, an operation to match a captured image and its associatedweights, such as a pairwise join, can be performed on the capturedimages 302 a, 302 b, 302 c and, respectively, the corresponding weights306 a, 306 b, 306 c. A high resolution image 310 can be generated fromthe captured images 302, 302 b, 302 c that have been weighted by thecorresponding weights 306 a, 306 b, 306 c. All examples herein areprovided for illustrative purposes, and there can be many variations andother possibilities.

FIG. 3B illustrates an example computing device 350 for generating abias associated with a camera sensor, according to an embodiment of thepresent disclosure. The computing device 350 can include a camera sensor352, a vibrating source 354, and a high frequency accelerometer 356. Forexample, the vibrating source 354 and the high frequency accelerometer356 can be connected to the camera sensor 352. In some embodiments, thecamera sensor 352, the vibrating source 354, and the high frequencyaccelerometer 356 can be implemented with the example bias generationmodule 112, 252, as discussed herein. The vibrating source 354 canintroduce a bias to the camera sensor 354, and the high frequencyaccelerometer 356 can measure the introduced bias. Multiple images of ascene captured by the camera sensor 352 with the introduced bias can beused to generate a high resolution image, for example, as described inconnection with the high resolution image module 102. All examplesherein are provided for illustrative purposes, and there can be manyvariations and other possibilities.

FIG. 4 illustrates an example first method 400 for increasing resolutionof captured images, according to an embodiment of the presentdisclosure. It should be understood that there can be additional, fewer,or alternative steps performed in similar or alternative orders, or inparallel, based on the various features and embodiments discussed hereinunless otherwise stated.

At block 402, the example method 400 can obtain information relating toa bias associated with a camera sensor. At block 404, the example method400 can obtain a plurality of images of a scene captured by the camerasensor, the plurality of images captured at a resolution supported bythe camera sensor. At block 406, the example method 400 can determine aplurality of weights for each image of the plurality of images based atleast in part on the bias. At block 408, the example method 400 cangenerate a combined image of the scene based on the plurality of imagesand the determined weights, the combined image having a resolutionhigher than the resolution supported by the camera sensor. Othersuitable techniques that incorporate various features and embodiments ofthe present disclosure are possible.

FIG. 5 illustrates an example second method 500 for increasingresolution of captured images, according to an embodiment of the presentdisclosure. It should be understood that there can be additional, fewer,or alternative steps performed in similar or alternative orders, or inparallel, based on the various features and embodiments discussed hereinunless otherwise stated. Certain steps of the method 500 may beperformed in combination with the example method 400 explained above.

At block 502, the example method 500 can provide a vibrating sourceconfigured to cause movement of a camera sensor to generate a bias forthe camera sensor. At block 504, the example method 500 can provide ahigh frequency accelerometer configured to measure movement of thecamera sensor. At block 506, the example method 500 can capture aplurality of images of a scene using the camera sensor based on thegenerated bias at a resolution supported by the camera sensor, whereinthe high frequency accelerometer measures movement of the camera sensorduring the capture of the plurality of images. Other suitable techniquesthat incorporate various features and embodiments of the presentdisclosure are possible.

It is contemplated that there can be many other uses, applications,features, possibilities, and/or variations associated with variousembodiments of the present disclosure. For example, users can, in somecases, choose whether or not to opt-in to utilize the disclosedtechnology. The disclosed technology can, for instance, also ensure thatvarious privacy settings, preferences, and configurations are maintainedand can prevent private information from being divulged. In anotherexample, various embodiments of the present disclosure can learn,improve, and/or be refined over time.

Social Networking System—Example Implementation

FIG. 6 illustrates a network diagram of an example system 600 that canbe utilized in various scenarios, in accordance with an embodiment ofthe present disclosure. The system 600 includes one or more user devices610, one or more external systems 620, a social networking system (orservice) 630, and a network 650. In an embodiment, the social networkingservice, provider, and/or system discussed in connection with theembodiments described above may be implemented as the social networkingsystem 630. For purposes of illustration, the embodiment of the system600, shown by FIG. 6, includes a single external system 620 and a singleuser device 610. However, in other embodiments, the system 600 mayinclude more user devices 610 and/or more external systems 620. Incertain embodiments, the social networking system 630 is operated by asocial network provider, whereas the external systems 620 are separatefrom the social networking system 630 in that they may be operated bydifferent entities. In various embodiments, however, the socialnetworking system 630 and the external systems 620 operate inconjunction to provide social networking services to users (or members)of the social networking system 630. In this sense, the socialnetworking system 630 provides a platform or backbone, which othersystems, such as external systems 620, may use to provide socialnetworking services and functionalities to users across the Internet.

The user device 610 comprises one or more computing devices that canreceive input from a user and transmit and receive data via the network650. In one embodiment, the user device 610 is a conventional computersystem executing, for example, a Microsoft Windows compatible operatingsystem (OS), Apple OS X, and/or a Linux distribution. In anotherembodiment, the user device 610 can be a device having computerfunctionality, such as a smart-phone, a tablet, a personal digitalassistant (PDA), a mobile telephone, etc. The user device 610 isconfigured to communicate via the network 650. The user device 610 canexecute an application, for example, a browser application that allows auser of the user device 610 to interact with the social networkingsystem 630. In another embodiment, the user device 610 interacts withthe social networking system 630 through an application programminginterface (API) provided by the native operating system of the userdevice 610, such as iOS and ANDROID. The user device 610 is configuredto communicate with the external system 620 and the social networkingsystem 630 via the network 650, which may comprise any combination oflocal area and/or wide area networks, using wired and/or wirelesscommunication systems.

In one embodiment, the network 650 uses standard communicationstechnologies and protocols. Thus, the network 650 can include linksusing technologies such as Ethernet, 802.11, worldwide interoperabilityfor microwave access (WiMAX), 3G, 4G, CDMA, GSM, LTE, digital subscriberline (DSL), etc. Similarly, the networking protocols used on the network650 can include multiprotocol label switching (MPLS), transmissioncontrol protocol/Internet protocol (TCP/IP), User Datagram Protocol(UDP), hypertext transport protocol (HTTP), simple mail transferprotocol (SMTP), file transfer protocol (FTP), and the like. The dataexchanged over the network 650 can be represented using technologiesand/or formats including hypertext markup language (HTML) and extensiblemarkup language (XML). In addition, all or some links can be encryptedusing conventional encryption technologies such as secure sockets layer(SSL), transport layer security (TLS), and Internet Protocol security(IPsec).

In one embodiment, the user device 610 may display content from theexternal system 620 and/or from the social networking system 630 byprocessing a markup language document 614 received from the externalsystem 620 and from the social networking system 630 using a browserapplication 612. The markup language document 614 identifies content andone or more instructions describing formatting or presentation of thecontent. By executing the instructions included in the markup languagedocument 614, the browser application 612 displays the identifiedcontent using the format or presentation described by the markuplanguage document 614. For example, the markup language document 614includes instructions for generating and displaying a web page havingmultiple frames that include text and/or image data retrieved from theexternal system 620 and the social networking system 630. In variousembodiments, the markup language document 614 comprises a data fileincluding extensible markup language (XML) data, extensible hypertextmarkup language (XHTML) data, or other markup language data.Additionally, the markup language document 614 may include JavaScriptObject Notation (JSON) data, JSON with padding (JSONP), and JavaScriptdata to facilitate data-interchange between the external system 620 andthe user device 610. The browser application 612 on the user device 610may use a JavaScript compiler to decode the markup language document614.

The markup language document 614 may also include, or link to,applications or application frameworks such as FLASH™ or Unity™applications, the SilverLight™ application framework, etc.

In one embodiment, the user device 610 also includes one or more cookies616 including data indicating whether a user of the user device 610 islogged into the social networking system 630, which may enablemodification of the data communicated from the social networking system630 to the user device 610.

The external system 620 includes one or more web servers that includeone or more web pages 622 a, 622 b, which are communicated to the userdevice 610 using the network 650. The external system 620 is separatefrom the social networking system 630. For example, the external system620 is associated with a first domain, while the social networkingsystem 630 is associated with a separate social networking domain. Webpages 622 a, 622 b, included in the external system 620, comprise markuplanguage documents 614 identifying content and including instructionsspecifying formatting or presentation of the identified content.

The social networking system 630 includes one or more computing devicesfor a social network, including a plurality of users, and providingusers of the social network with the ability to communicate and interactwith other users of the social network. In some instances, the socialnetwork can be represented by a graph, i.e., a data structure includingedges and nodes. Other data structures can also be used to represent thesocial network, including but not limited to databases, objects,classes, meta elements, files, or any other data structure. The socialnetworking system 630 may be administered, managed, or controlled by anoperator. The operator of the social networking system 630 may be ahuman being, an automated application, or a series of applications formanaging content, regulating policies, and collecting usage metricswithin the social networking system 630. Any type of operator may beused.

Users may join the social networking system 630 and then add connectionsto any number of other users of the social networking system 630 to whomthey desire to be connected. As used herein, the term “friend” refers toany other user of the social networking system 630 to whom a user hasformed a connection, association, or relationship via the socialnetworking system 630. For example, in an embodiment, if users in thesocial networking system 630 are represented as nodes in the socialgraph, the term “friend” can refer to an edge formed between anddirectly connecting two user nodes.

Connections may be added explicitly by a user or may be automaticallycreated by the social networking system 630 based on commoncharacteristics of the users (e.g., users who are alumni of the sameeducational institution). For example, a first user specifically selectsa particular other user to be a friend. Connections in the socialnetworking system 630 are usually in both directions, but need not be,so the terms “user” and “friend” depend on the frame of reference.Connections between users of the social networking system 630 areusually bilateral (“two-way”), or “mutual,” but connections may also beunilateral, or “one-way.” For example, if Bob and Joe are both users ofthe social networking system 630 and connected to each other, Bob andJoe are each other's connections. If, on the other hand, Bob wishes toconnect to Joe to view data communicated to the social networking system630 by Joe, but Joe does not wish to form a mutual connection, aunilateral connection may be established. The connection between usersmay be a direct connection; however, some embodiments of the socialnetworking system 630 allow the connection to be indirect via one ormore levels of connections or degrees of separation.

In addition to establishing and maintaining connections between usersand allowing interactions between users, the social networking system630 provides users with the ability to take actions on various types ofitems supported by the social networking system 630. These items mayinclude groups or networks (i.e., social networks of people, entities,and concepts) to which users of the social networking system 630 maybelong, events or calendar entries in which a user might be interested,computer-based applications that a user may use via the socialnetworking system 630, transactions that allow users to buy or sellitems via services provided by or through the social networking system630, and interactions with advertisements that a user may perform on oroff the social networking system 630. These are just a few examples ofthe items upon which a user may act on the social networking system 630,and many others are possible. A user may interact with anything that iscapable of being represented in the social networking system 630 or inthe external system 620, separate from the social networking system 630,or coupled to the social networking system 630 via the network 650.

The social networking system 630 is also capable of linking a variety ofentities. For example, the social networking system 630 enables users tointeract with each other as well as external systems 620 or otherentities through an API, a web service, or other communication channels.The social networking system 630 generates and maintains the “socialgraph” comprising a plurality of nodes interconnected by a plurality ofedges. Each node in the social graph may represent an entity that canact on another node and/or that can be acted on by another node. Thesocial graph may include various types of nodes. Examples of types ofnodes include users, non-person entities, content items, web pages,groups, activities, messages, concepts, and any other things that can berepresented by an object in the social networking system 630. An edgebetween two nodes in the social graph may represent a particular kind ofconnection, or association, between the two nodes, which may result fromnode relationships or from an action that was performed by one of thenodes on the other node. In some cases, the edges between nodes can beweighted. The weight of an edge can represent an attribute associatedwith the edge, such as a strength of the connection or associationbetween nodes. Different types of edges can be provided with differentweights. For example, an edge created when one user “likes” another usermay be given one weight, while an edge created when a user befriendsanother user may be given a different weight.

As an example, when a first user identifies a second user as a friend,an edge in the social graph is generated connecting a node representingthe first user and a second node representing the second user. Asvarious nodes relate or interact with each other, the social networkingsystem 630 modifies edges connecting the various nodes to reflect therelationships and interactions.

The social networking system 630 also includes user-generated content,which enhances a user's interactions with the social networking system630. User-generated content may include anything a user can add, upload,send, or “post” to the social networking system 630. For example, a usercommunicates posts to the social networking system 630 from a userdevice 610. Posts may include data such as status updates or othertextual data, location information, images such as photos, videos,links, music or other similar data and/or media. Content may also beadded to the social networking system 630 by a third party. Content“items” are represented as objects in the social networking system 630.In this way, users of the social networking system 630 are encouraged tocommunicate with each other by posting text and content items of varioustypes of media through various communication channels. Suchcommunication increases the interaction of users with each other andincreases the frequency with which users interact with the socialnetworking system 630.

The social networking system 630 includes a web server 632, an APIrequest server 634, a user profile store 636, a connection store 638, anaction logger 640, an activity log 642, and an authorization server 644.In an embodiment of the invention, the social networking system 630 mayinclude additional, fewer, or different components for variousapplications. Other components, such as network interfaces, securitymechanisms, load balancers, failover servers, management and networkoperations consoles, and the like are not shown so as to not obscure thedetails of the system.

The user profile store 636 maintains information about user accounts,including biographic, demographic, and other types of descriptiveinformation, such as work experience, educational history, hobbies orpreferences, location, and the like that has been declared by users orinferred by the social networking system 630. This information is storedin the user profile store 636 such that each user is uniquelyidentified. The social networking system 630 also stores data describingone or more connections between different users in the connection store638. The connection information may indicate users who have similar orcommon work experience, group memberships, hobbies, or educationalhistory. Additionally, the social networking system 630 includesuser-defined connections between different users, allowing users tospecify their relationships with other users. For example, user-definedconnections allow users to generate relationships with other users thatparallel the users' real-life relationships, such as friends,co-workers, partners, and so forth. Users may select from predefinedtypes of connections, or define their own connection types as needed.Connections with other nodes in the social networking system 630, suchas non-person entities, buckets, cluster centers, images, interests,pages, external systems, concepts, and the like are also stored in theconnection store 638.

The social networking system 630 maintains data about objects with whicha user may interact. To maintain this data, the user profile store 636and the connection store 638 store instances of the corresponding typeof objects maintained by the social networking system 630. Each objecttype has information fields that are suitable for storing informationappropriate to the type of object. For example, the user profile store636 contains data structures with fields suitable for describing auser's account and information related to a user's account. When a newobject of a particular type is created, the social networking system 630initializes a new data structure of the corresponding type, assigns aunique object identifier to it, and begins to add data to the object asneeded. This might occur, for example, when a user becomes a user of thesocial networking system 630, the social networking system 630 generatesa new instance of a user profile in the user profile store 636, assignsa unique identifier to the user account, and begins to populate thefields of the user account with information provided by the user.

The connection store 638 includes data structures suitable fordescribing a user's connections to other users, connections to externalsystems 620 or connections to other entities. The connection store 638may also associate a connection type with a user's connections, whichmay be used in conjunction with the user's privacy setting to regulateaccess to information about the user. In an embodiment of the invention,the user profile store 636 and the connection store 638 may beimplemented as a federated database.

Data stored in the connection store 638, the user profile store 636, andthe activity log 642 enables the social networking system 630 togenerate the social graph that uses nodes to identify various objectsand edges connecting nodes to identify relationships between differentobjects. For example, if a first user establishes a connection with asecond user in the social networking system 630, user accounts of thefirst user and the second user from the user profile store 636 may actas nodes in the social graph. The connection between the first user andthe second user stored by the connection store 638 is an edge betweenthe nodes associated with the first user and the second user. Continuingthis example, the second user may then send the first user a messagewithin the social networking system 630. The action of sending themessage, which may be stored, is another edge between the two nodes inthe social graph representing the first user and the second user.Additionally, the message itself may be identified and included in thesocial graph as another node connected to the nodes representing thefirst user and the second user.

In another example, a first user may tag a second user in an image thatis maintained by the social networking system 630 (or, alternatively, inan image maintained by another system outside of the social networkingsystem 630). The image may itself be represented as a node in the socialnetworking system 630. This tagging action may create edges between thefirst user and the second user as well as create an edge between each ofthe users and the image, which is also a node in the social graph. Inyet another example, if a user confirms attending an event, the user andthe event are nodes obtained from the user profile store 636, where theattendance of the event is an edge between the nodes that may beretrieved from the activity log 642. By generating and maintaining thesocial graph, the social networking system 630 includes data describingmany different types of objects and the interactions and connectionsamong those objects, providing a rich source of socially relevantinformation.

The web server 632 links the social networking system 630 to one or moreuser devices 610 and/or one or more external systems 620 via the network650. The web server 632 serves web pages, as well as other web-relatedcontent, such as Java, JavaScript, Flash, XML, and so forth. The webserver 632 may include a mail server or other messaging functionalityfor receiving and routing messages between the social networking system630 and one or more user devices 610. The messages can be instantmessages, queued messages (e.g., email), text and SMS messages, or anyother suitable messaging format.

The API request server 634 allows one or more external systems 620 anduser devices 610 to call access information from the social networkingsystem 630 by calling one or more API functions. The API request server634 may also allow external systems 620 to send information to thesocial networking system 630 by calling APIs. The external system 620,in one embodiment, sends an API request to the social networking system630 via the network 650, and the API request server 634 receives the APIrequest. The API request server 634 processes the request by calling anAPI associated with the API request to generate an appropriate response,which the API request server 634 communicates to the external system 620via the network 650. For example, responsive to an API request, the APIrequest server 634 collects data associated with a user, such as theuser's connections that have logged into the external system 620, andcommunicates the collected data to the external system 620. In anotherembodiment, the user device 610 communicates with the social networkingsystem 630 via APIs in the same manner as external systems 620.

The action logger 640 is capable of receiving communications from theweb server 632 about user actions on and/or off the social networkingsystem 630. The action logger 640 populates the activity log 642 withinformation about user actions, enabling the social networking system630 to discover various actions taken by its users within the socialnetworking system 630 and outside of the social networking system 630.Any action that a particular user takes with respect to another node onthe social networking system 630 may be associated with each user'saccount, through information maintained in the activity log 642 or in asimilar database or other data repository. Examples of actions taken bya user within the social networking system 630 that are identified andstored may include, for example, adding a connection to another user,sending a message to another user, reading a message from another user,viewing content associated with another user, attending an event postedby another user, posting an image, attempting to post an image, or otheractions interacting with another user or another object. When a usertakes an action within the social networking system 630, the action isrecorded in the activity log 642. In one embodiment, the socialnetworking system 630 maintains the activity log 642 as a database ofentries. When an action is taken within the social networking system630, an entry for the action is added to the activity log 642. Theactivity log 642 may be referred to as an action log.

Additionally, user actions may be associated with concepts and actionsthat occur within an entity outside of the social networking system 630,such as an external system 620 that is separate from the socialnetworking system 630. For example, the action logger 640 may receivedata describing a user's interaction with an external system 620 fromthe web server 632. In this example, the external system 620 reports auser's interaction according to structured actions and objects in thesocial graph.

Other examples of actions where a user interacts with an external system620 include a user expressing an interest in an external system 620 oranother entity, a user posting a comment to the social networking system630 that discusses an external system 620 or a web page 622 a within theexternal system 620, a user posting to the social networking system 630a Uniform Resource Locator (URL) or other identifier associated with anexternal system 620, a user attending an event associated with anexternal system 620, or any other action by a user that is related to anexternal system 620. Thus, the activity log 642 may include actionsdescribing interactions between a user of the social networking system630 and an external system 620 that is separate from the socialnetworking system 630.

The authorization server 644 enforces one or more privacy settings ofthe users of the social networking system 630. A privacy setting of auser determines how particular information associated with a user can beshared. The privacy setting comprises the specification of particularinformation associated with a user and the specification of the entityor entities with whom the information can be shared. Examples ofentities with which information can be shared may include other users,applications, external systems 620, or any entity that can potentiallyaccess the information. The information that can be shared by a usercomprises user account information, such as profile photos, phonenumbers associated with the user, user's connections, actions taken bythe user such as adding a connection, changing user profile information,and the like.

The privacy setting specification may be provided at different levels ofgranularity. For example, the privacy setting may identify specificinformation to be shared with other users; the privacy settingidentifies a work phone number or a specific set of related information,such as, personal information including profile photo, home phonenumber, and status. Alternatively, the privacy setting may apply to allthe information associated with the user. The specification of the setof entities that can access particular information can also be specifiedat various levels of granularity. Various sets of entities with whichinformation can be shared may include, for example, all friends of theuser, all friends of friends, all applications, or all external systems620. One embodiment allows the specification of the set of entities tocomprise an enumeration of entities. For example, the user may provide alist of external systems 620 that are allowed to access certaininformation. Another embodiment allows the specification to comprise aset of entities along with exceptions that are not allowed to access theinformation. For example, a user may allow all external systems 620 toaccess the user's work information, but specify a list of externalsystems 620 that are not allowed to access the work information. Certainembodiments call the list of exceptions that are not allowed to accesscertain information a “block list”. External systems 620 belonging to ablock list specified by a user are blocked from accessing theinformation specified in the privacy setting. Various combinations ofgranularity of specification of information, and granularity ofspecification of entities, with which information is shared arepossible. For example, all personal information may be shared withfriends whereas all work information may be shared with friends offriends.

The authorization server 644 contains logic to determine if certaininformation associated with a user can be accessed by a user's friends,external systems 620, and/or other applications and entities. Theexternal system 620 may need authorization from the authorization server644 to access the user's more private and sensitive information, such asthe user's work phone number. Based on the user's privacy settings, theauthorization server 644 determines if another user, the external system620, an application, or another entity is allowed to access informationassociated with the user, including information about actions taken bythe user.

In some embodiments, the social networking system 630 can include a highresolution image module 646. The high resolution image module 646 can beimplemented with the high resolution image module 102, as discussed inmore detail herein. In some embodiments, one or more functionalities ofthe high resolution image module 646 can be implemented in the userdevice 610.

In some embodiments, the user device 610 can include a bias generationmodule 618. The bias generation module 618 can be implemented with thebias generation module 112, as discussed in more detail herein. In someembodiments, one or more functionalities of the bias generation module618 can be implemented in the social networking system 630.

Hardware Implementation

The foregoing processes and features can be implemented by a widevariety of machine and computer system architectures and in a widevariety of network and computing environments. FIG. 7 illustrates anexample of a computer system 700 that may be used to implement one ormore of the embodiments described herein in accordance with anembodiment of the invention. The computer system 700 includes sets ofinstructions for causing the computer system 700 to perform theprocesses and features discussed herein. The computer system 700 may beconnected (e.g., networked) to other machines. In a networkeddeployment, the computer system 700 may operate in the capacity of aserver machine or a client machine in a client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. In an embodiment of the invention, the computersystem 700 may be the social networking system 630, the user device 610,and the external system 720, or a component thereof. In an embodiment ofthe invention, the computer system 700 may be one server among many thatconstitutes all or part of the social networking system 630.

The computer system 700 includes a processor 702, a cache 704, and oneor more executable modules and drivers, stored on a computer-readablemedium, directed to the processes and features described herein.Additionally, the computer system 700 includes a high performanceinput/output (I/O) bus 706 and a standard I/O bus 708. A host bridge 710couples processor 702 to high performance I/O bus 706, whereas I/O busbridge 712 couples the two buses 706 and 708 to each other. A systemmemory 714 and one or more network interfaces 716 couple to highperformance I/O bus 706. The computer system 700 may further includevideo memory and a display device coupled to the video memory (notshown). Mass storage 718 and I/O ports 720 couple to the standard I/Obus 708. The computer system 700 may optionally include a keyboard andpointing device, a display device, or other input/output devices (notshown) coupled to the standard I/O bus 708. Collectively, these elementsare intended to represent a broad category of computer hardware systems,including but not limited to computer systems based on thex86-compatible processors manufactured by Intel Corporation of SantaClara, Calif., and the x86-compatible processors manufactured byAdvanced Micro Devices (AMD), Inc., of Sunnyvale, Calif., as well as anyother suitable processor.

An operating system manages and controls the operation of the computersystem 700, including the input and output of data to and from softwareapplications (not shown). The operating system provides an interfacebetween the software applications being executed on the system and thehardware components of the system. Any suitable operating system may beused, such as the LINUX Operating System, the Apple Macintosh OperatingSystem, available from Apple Computer Inc. of Cupertino, Calif., UNIXoperating systems, Microsoft® Windows® operating systems, BSD operatingsystems, and the like. Other implementations are possible.

The elements of the computer system 700 are described in greater detailbelow. In particular, the network interface 716 provides communicationbetween the computer system 700 and any of a wide range of networks,such as an Ethernet (e.g., IEEE 802.3) network, a backplane, etc. Themass storage 718 provides permanent storage for the data and programminginstructions to perform the above-described processes and featuresimplemented by the respective computing systems identified above,whereas the system memory 714 (e.g., DRAM) provides temporary storagefor the data and programming instructions when executed by the processor702. The I/O ports 720 may be one or more serial and/or parallelcommunication ports that provide communication between additionalperipheral devices, which may be coupled to the computer system 700.

The computer system 700 may include a variety of system architectures,and various components of the computer system 700 may be rearranged. Forexample, the cache 704 may be on-chip with processor 702. Alternatively,the cache 704 and the processor 702 may be packed together as a“processor module”, with processor 702 being referred to as the“processor core”. Furthermore, certain embodiments of the invention mayneither require nor include all of the above components. For example,peripheral devices coupled to the standard I/O bus 708 may couple to thehigh performance I/O bus 706. In addition, in some embodiments, only asingle bus may exist, with the components of the computer system 700being coupled to the single bus. Moreover, the computer system 700 mayinclude additional components, such as additional processors, storagedevices, or memories.

In general, the processes and features described herein may beimplemented as part of an operating system or a specific application,component, program, object, module, or series of instructions referredto as “programs”. For example, one or more programs may be used toexecute specific processes described herein. The programs typicallycomprise one or more instructions in various memory and storage devicesin the computer system 700 that, when read and executed by one or moreprocessors, cause the computer system 700 to perform operations toexecute the processes and features described herein. The processes andfeatures described herein may be implemented in software, firmware,hardware (e.g., an application specific integrated circuit), or anycombination thereof.

In one implementation, the processes and features described herein areimplemented as a series of executable modules run by the computer system700, individually or collectively in a distributed computingenvironment. The foregoing modules may be realized by hardware,executable modules stored on a computer-readable medium (ormachine-readable medium), or a combination of both. For example, themodules may comprise a plurality or series of instructions to beexecuted by a processor in a hardware system, such as the processor 702.Initially, the series of instructions may be stored on a storage device,such as the mass storage 718. However, the series of instructions can bestored on any suitable computer readable storage medium. Furthermore,the series of instructions need not be stored locally, and could bereceived from a remote storage device, such as a server on a network,via the network interface 716. The instructions are copied from thestorage device, such as the mass storage 718, into the system memory 714and then accessed and executed by the processor 702. In variousimplementations, a module or modules can be executed by a processor ormultiple processors in one or multiple locations, such as multipleservers in a parallel processing environment.

Examples of computer-readable media include, but are not limited to,recordable type media such as volatile and non-volatile memory devices;solid state memories; floppy and other removable disks; hard diskdrives; magnetic media; optical disks (e.g., Compact Disk Read-OnlyMemory (CD ROMS), Digital Versatile Disks (DVDs)); other similarnon-transitory (or transitory), tangible (or non-tangible) storagemedium; or any type of medium suitable for storing, encoding, orcarrying a series of instructions for execution by the computer system700 to perform any one or more of the processes and features describedherein.

For purposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the description. It will beapparent, however, to one skilled in the art that embodiments of thedisclosure can be practiced without these specific details. In someinstances, modules, structures, processes, features, and devices areshown in block diagram form in order to avoid obscuring the description.In other instances, functional block diagrams and flow diagrams areshown to represent data and logic flows. The components of blockdiagrams and flow diagrams (e.g., modules, blocks, structures, devices,features, etc.) may be variously combined, separated, removed,reordered, and replaced in a manner other than as expressly describedand depicted herein.

Reference in this specification to “one embodiment”, “an embodiment”,“other embodiments”, “one series of embodiments”, “some embodiments”,“various embodiments”, or the like means that a particular feature,design, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of, for example, the phrase “in one embodiment” or “in anembodiment” in various places in the specification are not necessarilyall referring to the same embodiment, nor are separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, whetheror not there is express reference to an “embodiment” or the like,various features are described, which may be variously combined andincluded in some embodiments, but also variously omitted in otherembodiments. Similarly, various features are described that may bepreferences or requirements for some embodiments, but not otherembodiments.

The language used herein has been principally selected for readabilityand instructional purposes, and it may not have been selected todelineate or circumscribe the inventive subject matter. It is thereforeintended that the scope of the invention be limited not by this detaileddescription, but rather by any claims that issue on an application basedhereon. Accordingly, the disclosure of the embodiments of the inventionis intended to be illustrative, but not limiting, of the scope of theinvention, which is set forth in the following claims.

What is claimed is:
 1. A computer-implemented method comprising:generating, by a computing system, a bias associated with a camerasensor using a vibrating source; obtaining, by the computing system,information relating to the bias from an accelerometer associated withthe camera sensor, wherein the bias includes a random bias; obtaining,by the computing system, a plurality of images of a scene captured bythe camera sensor, the plurality of images captured at a resolutionsupported by the camera sensor and aligned with respect to the camerasensor; determining, by the computing system, a plurality of weights foreach image of the plurality of images based at least in part on thebias; and generating, by the computing system, a combined image of thescene based on the plurality of images and the determined weights, thecombined image having a resolution higher than the resolution supportedby the camera sensor.
 2. The computer-implemented method of claim 1,wherein the bias provides subpixel image data for a portion of an imagecaptured by the camera sensor.
 3. The computer-implemented method ofclaim 1, wherein the bias further includes one or more of: a directionalbias or an average bias.
 4. The computer-implemented method of claim 1,wherein each image of the plurality of images includes a random set ofpixels captured by the camera sensor.
 5. The computer-implemented methodof claim 1, wherein the plurality of weights for each image includes aweight for each pixel of the image.
 6. The computer-implemented methodof claim 1, wherein the plurality of weights for each image isdetermined based on a machine learning model.
 7. Thecomputer-implemented method of claim 1, wherein, for a particularsection of the combined image, a weight assigned to a portion of animage of the plurality of images that aligns with the particular sectionis higher than a weight assigned to a portion of an image of theplurality of images that does not align with the particular section. 8.The computer-implemented method of claim 1, wherein a weight assigned toa portion of an image of the plurality of images that includes the biasis higher than a weight assigned to a portion of the image that does notinclude the bias.
 9. The computer-implemented method of claim 1, whereinthe generating the combined image of the scene includes performing ajoin on the plurality of images and the corresponding plurality ofweights.
 10. A system comprising: at least one hardware processor; and amemory storing instructions that, when executed by the at least oneprocessor, cause the system to perform: generating a bias associatedwith a camera sensor using a vibrating source; obtaining informationrelating to the bias from an accelerometer associated with the camerasensor, wherein the bias includes a random bias; obtaining a pluralityof images of a scene captured by the camera sensor, the plurality ofimages captured at a resolution supported by the camera sensor andaligned with respect to the camera sensor; determining a plurality ofweights for each image of the plurality of images based at least in parton the bias; and generating a combined image of the scene based on theplurality of images and the determined weights, the combined imagehaving a resolution higher than the resolution supported by the camerasensor.
 11. The system of claim 10, wherein the bias provides subpixelimage data for a portion of an image captured by the camera sensor. 12.The system of claim 10, wherein the bias further includes one or moreof: a directional bias or an average bias.
 13. The system of claim 10,wherein, for a particular section of the combined image, a weightassigned to a portion of an image of the plurality of images that alignswith the particular section is higher than a weight assigned to aportion of an image of the plurality of images that does not align withthe particular section.
 14. The system of claim 10, wherein a weightassigned to a portion of an image of the plurality of images thatincludes the bias is higher than a weight assigned to a portion of theimage that does not include the bias.
 15. A non-transitory computerreadable medium including instructions that, when executed by at leastone hardware processor of a computing system, cause the computing systemto perform a method comprising: generating a bias associated with acamera sensor using a vibrating source; obtaining information relatingto the bias from an accelerometer associated with the camera sensor,wherein the bias includes a random bias; obtaining a plurality of imagesof a scene captured by the camera sensor, the plurality of imagescaptured at a resolution supported by the camera sensor and aligned withrespect to the camera sensor; determining a plurality of weights foreach image of the plurality of images based at least in part on thebias; and generating a combined image of the scene based on theplurality of images and the determined weights, the combined imagehaving a resolution higher than the resolution supported by the camerasensor.
 16. The non-transitory computer readable medium of claim 15,wherein the bias provides subpixel image data for a portion of an imagecaptured by the camera sensor.
 17. The non-transitory computer readablemedium of claim 15, wherein the bias further includes one or more of: adirectional bias or an average bias.
 18. The non-transitory computerreadable medium of claim 15, wherein, for a particular section of thecombined image, a weight assigned to a portion of an image of theplurality of images that aligns with the particular section is higherthan a weight assigned to a portion of an image of the plurality ofimages that does not align with the particular section.
 19. Thenon-transitory computer readable medium of claim 15, wherein a weightassigned to a portion of an image of the plurality of images thatincludes the bias is higher than a weight assigned to a portion of theimage that does not include the bias.